Intelligent tutoring systems often emphasize learning efficiency, but learner engagement and continued participation also affect the learning experience. Predicting dropout within an ongoing mobile study session differs from predicting withdrawal from a school or MOOC because mobile applications, messaging, and other interruptions can disrupt a session. In this study, consecutive interactions belong to the same study session when they are separated by no more than one hour; a longer period of inactivity ends the session and is treated as dropout. The paper proposes the transformer-based Deep Attentive Study (DAS) model and evaluates it using mobile interactions from the Santa dataset.